Combined Quantum Mechanics/Molecular Mechanics (QM/MM) Methods in Computational Enzymology Marc W. van der Kamp* and Adrian J. Mulholland* Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K. ABSTRACT: Computational enzymology is a rapidly maturing field that is increasingly integral to understanding mechanisms of enzyme-catalyzed reactions and their practical applications. Combined quantum mechanics/molecular mechanics (QM/MM) methods are important in this field. By treating the reacting species with a quantum mechanical method (i.e., a method that calculates the electronic structure of the active site) and including the enzyme environment with simpler molecular mechanical methods, enzyme reactions can be modeled. Here, we review QM/MM methods and their application to enzyme-catalyzed reactions to investigate fundamental and practical problems in enzymology. A range of QM/MM methods is available, from cheaper and more approximate methods, which can be used for molecular dynamics simulations, to highly accurate electronic structure methods. We discuss how modeling of reactions using such methods can provide detailed insight into enzyme mechanisms and illustrate this by reviewing some recent applications. We outline some practical considerations for such simulations. Further, we highlight applications that show how QM/MM methods can contribute to the practical development and application of enzymology, e.g., in the interpretation and prediction of the effects of mutagenesis and in drug and catalyst design. E nzymes are both essential and extraordinary due to their phenomenal capability to catalyze biochemical reactions efficiently, typically with high specificity and under mild, physiological conditions. Understanding how enzymes achieve these remarkable feats is not only one of the most important fundamental problems in biology, it will also contribute to a range of technological applications such as designing inhibitors that serve as lead compounds in drug discovery, predicting the metabolism of drugs, and designing catalysts for specific transformations. A wide variety of experiments in structural biology, enzyme kinetics, and mutagenesis have given insight into enzymes. Because of the complexity of enzymes and the difficulty of studying reactions in them, however, many questions and uncertainties remain, giving rise to many heated debates in enzymology. Computational modeling and simu- lation, with their unique potential to offer detailed, atomic- resolution insight into the dynamics and reactions of biomolecules, 1 can help resolve such controversial questions by interpreting, complementing, and expanding results obtained from experiment. Perhaps most obviously, calculations can study transition state structures, which are central to reactivity but cannot be studied directly by experiments on enzymes. Computational enzymology can be defined broadly as the study of enzymes and their reaction mechanisms by molecular modeling and simulation. This field has matured rapidly in recent years, and increasingly experimental and computational enzymologists are collaborating to explain experimental data (see, e.g., refs 2 and 3) and use insights from modeling to guide further experiments. A number of different types of simulation have proved useful in computational enzymology. Combined quantum mechanics/molecular mechanics (QM/MM) meth- ods have been involved in this field, 4,5 ever since the pioneering work of Warshel and Levitt in 1976. 6 The desire to model reactions within enzymes has been an important driving force in the development of QM/MM methods. This review will primarily focus on QM/MM methods in computational enzymology; other simulation and modeling methods are also important in this field. In particular, the empirical valence bond (EVB) approach (which typically uses a combination of molecular mechanics representations rather than a molecular mechanics and an electronic structure QM method) has provided many fundamental insights into enzyme catalysis. 7-9 Calculations that employ QM methods only 10 also provide a good route to modeling many enzyme mechanisms, differing from QM/MM calculations mostly in the size of the system that can be modeled. In this review, we discuss different types of QM/MM methods, their scope, and practical considerations in their application to modeling enzyme reactions. We indicate how QM/MM methods have contributed to debates on the sources of enzyme catalytic power and provide detailed insight into individual mechanisms. We further highlight how modeling of reactions with QM/MM methods is contributing to developments in drug design, drug metabolism, and biocatalyst design. QM/MM methods are also being applied to other types of problems in biomolecular science, e.g., in the calculation of spectroscopic properties, photochemistry, pK a ’s, and predic- tions of ligand binding affinities in docking and free energy simulations, 11-17 but such applications are outside the scope of this review. Received: February 19, 2013 Revised: April 2, 2013 Current Topic pubs.acs.org/biochemistry © XXXX American Chemical Society A dx.doi.org/10.1021/bi400215w | Biochemistry XXXX, XXX, XXX-XXX